3D scanning is a very effective technology for producing millions of spatial measurement points of objects within minutes or seconds. Typical measurement tasks are the recording of objects or the surfaces thereof such as industrial plants, house facades or historical buildings, but also accident sites and crime scenes. Surveying apparatuses with scanning functionality are, for example, total stations and laser scanners, such as the Leica P20 or Leica Multi Station 50, which are used to measure or create 3D coordinates of surfaces. For this purpose, they have to be able to guide the measurement beam of a distance measuring device over a surface and in the process simultaneously to detect direction and distance with respect to the measurement point. From the distance and the direction information correlated therewith for each point, a so-called 3D point cloud is generated by means of data processing.
In terms of the fundamental structure, such terrestrial scanners are thus designed to detect a distance to an object point as measurement point using a, usually electro-optical and laser-based, distance measuring device. A direction deflecting unit likewise present is in this case designed in such a way that the measurement beam of the distance measuring device is deflected in at least two independent spatial directions, as a result of which a spatial measurement region can be recorded. The deflecting unit can be realized in the form of a moving mirror or alternatively also by other elements suitable for controlled angular deflection of optical radiation, such as, for example, rotatable prisms, movable optical waveguides, deformable optical components, etc. The measurement is usually effected with determination of distance and angles, that is to say in spherical coordinates, which can also be transformed into Cartesian coordinates for display and further processing. The distance measuring device can be embodied for example according to the principles of time-of-flight (TOF), phase, waveform digitizer (WFD) or interferometric measurement. For fast and accurate scanners, in particular a short measurement time in conjunction with high measurement accuracy is required, for example a distance accuracy in the mm range or below with measurement times of the individual points in the sub-microseconds to milliseconds range. In this case, the measurement region ranges from a few centimeters up to a few kilometers.
The spatial measurement resolution is of particular importance in this case. It determines what details can still be identified, but also the duration of the scanning process and the volume of data obtained in the process. Measurement projects with modern high-speed scanners produce 3D point clouds having a cardinality of, for example, hundreds of millions or billions of object points and beyond. The storage, transmission and processing of the enormous volume of data associated therewith poses great challenges for hardware and software. For example, the execution speed of programs for evaluating the 3D data is greatly dependent on the number of scanning points. It would therefore be advantageous if only those points or data that are actually of relevance to the respective measurement task were recorded.
In the case of scanning processes according to the prior art, however, it is by contrast usually the case that the acquired volume of data is unnecessarily inflated. In the case of scanners according to the prior art, attempts are made to achieve a point distance predefined by the user with respect to a predefined distance, i.e. a predefined initiation, by means of angular deflection with equal steps, that is to say that a distance measurement is initiated in each case at identical angular distances. The desired point distance would thus be achieved, however, on all surfaces to be measured only if the scanner were situated at the midpoint of a sphere. In actual fact, in reality depending on the distance and alignment of the object surfaces there arise diverging point distances, including extremely small point distances and thus extreme point densities in the near region of the surveying apparatus, particularly at the zenith and around the installation location. Especially these near-region measurement points are usually not of interest at all, since e.g. the floor or the ceiling of the measurement environment do not constitute an object to be surveyed.
Additional data ballast results from the fact that in the case of very small measurement distances the point distances in the near region, on account of the defined angular distances, may be even smaller than the diameter of the measurement beam, and so such point distances lead to redundant measurements and therefore do not bring any gain in spatial resolution even in the case of inherently desired measurement objects. Furthermore, during a scan in part points are generated which, although inherently a plus in terms of spatial resolution, nevertheless usually constitute unnecessary data since they do not yield a genuine gain in information. This involves e.g. points on a (non-curved) plane since the latter is ideally sufficiently defined by three points (which do not lie on a straight line), for which reason a relatively small point density would be sufficient in such regions.
Consequently, with regard to volume of data and also scanning duration it is usually unnecessary and inefficient and therefore undesirable to detect the scanning region with the highest possible resolution. On the other hand, as a result, under certain circumstances, depending on distance to the scanner and setpoint resolution chosen, highly relevant regions are scanned with such a low spatial resolution that there is an information deficit. Such regions of particularly high relevance are e.g. edges, curved planes or other shape changes. It would be desirable for such highly relevant object locations to be scanned in a targeted manner with an increased point density or reduced distance between the 3D points compared with other regions of the 3D point cloud, in order that these are imaged with as much detail as possible.
The prior art discloses a multiplicity of methods by which, in post-processing, after conclusion of the measurement and using external powerful computers, the data of the 3D point cloud can be processed, e.g. by filtering, smoothing or interpolation. However, e.g. a computational increase in the accuracy of the 3D points is possible or helpful only to a limited extent, since e.g. the profile of discontinuous locations such as object edges remains speculative. Furthermore, it is disadvantageous that superfluous data also have to be stored until then and processed, such that e.g. the requirements made of the data processing capacities of the surveying apparatus or the time expenditure for data transfer and processing until the conclusion of post-processing still remain very stringent.
By contrast, WO 2013/12826 discloses a method for a stationary laser scanner for data handling in real time, i.e. actually during data recording in the course of scanning. This involves carrying out, in the course of the scan, an areal segmentation of the 3D raw data into individual layers with a lattice structure passing through the layers for a respectively separate coding of the measurement components distance to the surface point, (both) alignment angles of the measurement beam and intensity of the received measurement radiation. The Codec for data transformation and compression is preferably a ZIP compression algorithm for distance and alignment angles and a JPEG compression algorithm for the intensity. For the intensity, the disclosure of WO 2013/12826 considers a compression of the data, which is associated with a partial loss of information or precision, as is the case when using a JPEG compression algorithm. By contrast, the disclosure of WO 2013/12826 explicitly teaches that the compression of the further components (distance, angles) is effected without loss of information or precision, as e.g. in the case of a compression based on a ZIP algorithm. In other words what is carried out (with the exception of the data concerning the intensity) is lossless storage of the data and hence no reduction of the data in the actual sense. That is to say that, disadvantageously, inherently unnecessary or undesired data are not sorted out, rather the volume of data to be stored or transferred is merely reduced by skilful organization and compression of the data. Sorting out scanned points is disclosed in WO 2013/12826 only for the case where the scanning speed is not optimally coordinated with the segmentation of the data, such that without sorting out the data organization structure would be disturbed, e.g. if a line of the structure lattice were otherwise filled doubly on account of an excessively low scanning speed. The scan data are thus adapted to the lattice structure serving for data segmentation, while the lattice structure remains strictly fixed. Apart from this exception, i.e. in the case of an optimum or error-free scan, the final data of the 3D points that are stored in the read only memory correspond to the directly recorded point data in terms of contents.